High-speed signal search method, device, and recording medium for the same

ABSTRACT

The object of the present invention is to provide a high-speed signal search method, device, and a recording medium for the same that can obtain detection results equivalent to precisely moving a window over the entire region of the input signal even when there is not precise movement of a window over the entire signal. The present invention provides a first step that generates a feature quantity sequence for a pre-recorded reference signal; a second step that sets the input signal window for the input signal that has been input; a third step that generates a feature quantity sequence for the input signal of this input signal window; a fourth step that calculates the input signal similarity value showing the degree of similarity between the feature quantity sequence generated in the first step and the feature quantity sequence generated in the third step; a fifth step that calculates the skip width showing the amount that the input signal can be moved; and a sixth step that determines the position of the input signal window based on the skip width calculated in the fifth step, sets the input signal window to this position, and calculates the input signal similarity value for each position of the input signal window by repeating the third step to the sixth step; and further, determines whether or not the reference signal exists at the position that the input signal window presently shows in the input signal based on the result of comparing the input signal similarity value and the predetermined threshold value.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to a high-speed signal search method, device, andrecording medium (which records this method) that searches a signalsequence for the location of a signal that is similar to a pre-recordedsignal. This recording medium records a program for executing bycomputer this high-speed signal search method, and is computer readable.The present invention can be used, for example, for audio signaldetection. That is, the present invention relates to signal detectiontechnology which can detect and automatically record the time aspecified commercial was broadcast in a broadcast audio signal, andstart and stop video recording by detecting a specified theme song.

In addition, the present invention is related to technology that canautomatically monitor the timing of applause, the timing of laughter,etc. in a broadcast, and search for a specified scene. Furthermore, thepresent invention can be applied to the detection of signals in general(video signals, etc.), not just audio signals.

This application is based patent applications filed in Japan (JapanesePatent Application, No. Hei 10-151723, Japanese Patent Application, No.Hei 10-244162, and Japanese Patent Application, No. Hei 11-49184), thecontents of which are incorporated herein by reference.

2. Prior Art

A matched filter is a conventional technique of detecting in a signalsequence a region having a desired signal (referred to hereinbelow as a“reference signal”). A matched filter is a technique wherein acorrelation between the waveform of the area of the object input signal(referred to hereinbelow as a “window”) and the waveform of a referencesignal is calculated as the window moves, and when the correlation valueexceeds a fixed value, it is determined that the reference signal iswithin the window.

However, in this method, there is the problem that because it isnecessary to calculate the correlation value while precisely moving theposition of the window with respect to the entire area of the inputsignal, the amount of calculation becomes enormous, and the calculationspeed becomes slow.

In contrast, instead of using the correlation value between the inputsignal waveform and a reference signal waveform, there is also thetechnique of calculating the feature quantities (that is, a numericalvalue or a set of numerical values indicating the feature of the signalwaveform) of the input signal waveform in this window, and carrying outa comparison between this input signal waveform and the reference signalwaveform by using, for example, the correlation value or Euclideandistance between this feature quantity and the feature quantity of areference signal waveform calculated in advance.

However, in this method as well, there is the problem that it isnecessary to calculate the feature quantities while precisely moving theposition of the window with respect to the entire area of the inputsignal, and because it is necessary to compare feature quantities, theamount of calculation becomes enormous, and the speed of calculation isslow.

SUMMARY OF THE INVENTION

In consideration of the above-described problems, it is an object of thepresent invention to provide a high-speed signal search method, device,and recording medium for the same which can obtain a detection resultequivalent to the case of precisely moving with respect to the entirearea even if the window is not precisely moved with respect to theentire area of the input signal.

In addition, another object of the present invention is to provide ahigh-speed signal search method, device, and recording medium for thesame which can detect a signal with higher precision even when it isdifficult to discriminate the signal with average features or when thesignal fluctuates due to noise, etc.

Furthermore, another object of the present invention is to provide ahigh-speed signal search method, device, and recording medium for thesame which can detect a signal with less processing than conventionallyeven when the signal is detected based on a plurality of referencesignals.

This invention provides a first step that generates a feature quantitysequence for a pre-recorded reference signal, a second step that setsthe input signal window for the input signal that has been input, athird step that generates a feature quantity sequence for the inputsignal of this input signal window, a fourth step that calculates theinput signal similarity value showing the degree of similarity betweenthe feature quantity sequence generated in the first step and thefeature quantity sequence generated in the third step, a fifth step thatcalculates the skip width showing the amount that the input signalwindow can be moved based on the input signal similarity valuecalculated in the fourth step, and a sixth step that determines theposition of the input signal window based on the skip width calculatedin the fifth step, sets the input signal window to this position, andfurther calculates the input signal similarity value for each positionof the input signal window by repeating the third step to the sixthstep, and determines whether or not the reference signal exists at theposition that the input signal window presently shows in the inputsignal based on the result of comparing the input signal similarityvalue and the predetermined threshold value.

According to another feature, the present invention provides a firststep that generates a feature quantity sequence for a pre-recordedreference signal, a second step that produces a histogram of the featurequantity generated in the first step, a third step that sets the inputsignal window for the input signal that has been input, a fourth stepthat generates a feature quantity sequence for the input signal of thisinput signal window, a fifth step that produces a histogram of thefeature quantity sequence generated in the fourth step, a sixth stepthat calculates the input signal similarity value showing the degree ofsimilarity between the histogram produced in the second step and thehistogram produced in the fifth step, a seventh step that calculates askip width indicating the amount that the input signal window can bemoved based on the input signal similarity value calculated in the sixthstep, and an eight step that determines the position of the input signalwindow based on the skip width calculated in the seventh step, and setsthe input signal window at that position, and further, calculates theinput signal similarity value for each position of the input signalwindow by repeating the fourth step to the eighth step, and determineswhether or not the reference signal exists at the position that theinput signal window presently shows in the input signal based on theresult of comparing the input signal similarity value and thepredetermined threshold value.

According to another feature, the present invention provides a firststep that generates a feature quantity sequence for a pre-recordedreference signal, a second step that sets the reference signal windowfor the feature quantity sequence generated in the first step, a thirdstep that partitions the reference signal window set in the second stepinto a plurality of partitioned reference signal windows, a fourth stepthat generates a feature quantity sequence for the input signal that hasbeen input, a fifth step that sets the input signal window for thefeature quantity sequence generated in the fourth step, a sixth stepthat partitions this input signal window into a plurality of partitionedinput signal windows corresponding to the plurality of partitionedreference signal windows, a seventh step that calculates the inputsignal similarity value showing the degree of similarity between thefeature quantity sequence of each reference signal partition window andthe feature quantity sequence in the partitioned input signal windowscorresponding to the relevant partitioned reference signal window, aneighth step that calculates a skip width indicating the amount that theinput signal window can be moved based on the input signal similarityvalue calculated in the seventh step, and a ninth step which determinesthe position of the input signal window based on the skip widthcalculated in the eighth step and sets the input signal window at thatposition, and further, calculates an input signal similarity value foreach position of the input signal window by repeating the sixth step tothe ninth step, and determines whether or not the reference signalexists at the position that the input signal window presently shows inthe input signal based on the result of comparing the input signalsimilarity value and the predetermined threshold value.

According to another feature, the present invention provides a firststep that generates a feature quantity sequence for a pre-recordedreference signal, a second step that sets a reference signal window forthe feature quantity sequence generated in the first step, a third stepthat partitions the reference signal window set in the second step intoa plurality of partitioned reference signal windows, a fourth step thatgenerates a feature quantity sequence for the input signal that has beeninput, a fifth step that sets the input signal window for the featurequantity sequence generated in the fourth step, a sixth step thatpartitions this input signal window into a plurality if partitionedinput signal windows corresponding to this plurality of partitionedreference signal windows, a seventh step that produces a histogram ofthe feature quantity sequence of each partitioned reference signalwindow, an eighth step the produces a histogram of a feature quantitysequence of each partitioned input signal window, a ninth step thatcalculates the input signal similarity value showing the degree ofsimilarity between the histogram of each partitioned reference signalwindow and the histogram of the partitioned input signal windowcorresponding to the relevant partitioned reference signal window, atenth step that calculates the skip width showing the amount that theinput signal window can move based on the input signal similarity valuecalculated in the ninth step, and an eleventh step that determines theposition of the input signal window and sets the input signal window atthat position, and further, calculates an input signal similarity valuefor each position of the input signal window by repeating the sixth stepto the eleventh step, and determines whether or not the reference signalexists at the position that the input signal window presently shows inthe input signal based on the result of comparing the input signalsimilarity value and the predetermined threshold value.

According to another feature, the present invention provides a firststep that generates a feature quantity sequence for a plurality ofpre-recorded reference signals, a second step that generates a featurequantity sequence for the input signal that has been input, a third stepthat sets the input signal window for the feature quantity sequencegenerated in the second step, a fourth step that calculates aninter-reference signal similarity value that shows the degree ofsimilarity between the feature quantity sequence related to a formerreference signal and the feature quantity sequence related to a laterreference signal for two reference signals among the plurality ofreference signals, a fifth step that calculates the input signalsimilarity value showing the degree of similarity between the featurequantity sequence generated in the first step and the feature quantitysequence in the said input signal window for each reference signal amongsaid plurality of reference signals, a sixth step that calculates a skipwidth showing the amount that the input signal window can move based onthe inter-reference signal similarity value calculated in the fourthstep and the input signal similarity value calculated in the fifth step,and a seventh step that determines the position of the input signalwindow based on the skip width calculated in the sixth step and sets theinput signal window at that position, and further, calculates an inputsignal similarity value for each position of the input signal window byrepeating the fifth step to the seventh step, and determines whether ornot the reference signal exists at the position that the input signalwindow presently shows in the input signal based on the result ofcomparing the input signal similarity value and the predeterminedthreshold value.

According to another feature, the present invention provides a firststep that generates a feature quantity sequence for a plurality ofpre-recorded reference signals, a second step that produces histogramsfor the feature quantity sequences generated in the first step, a thirdstep that generates a feature quantity sequence for an input signal thathas been input, a fourth step that sets the input signal window for thefeature quantity sequence generated in the third step, a fifth step thatproduces histograms for the feature quantity sequences of the inputsignal window, a sixth step that calculates an inter-reference signalsimilarity value showing the degree of similarity between the histogramrelated to a former reference signal and the histogram related to alater reference signal for two reference signals among the plurality ofreference signals, a seventh step that calculates the input signalsimilarity value showing the degree of similarity between the histogramsgenerated in the second step and the histograms generated in the fifthstep for each reference signal among the plurality of reference signals,an eighth step that calculates a skip width showing the amount that theinput signal window can move based on the inter-reference signalsimilarity value calculated in the sixth step and the input signalsimilarity value calculated in the seventh step, and a ninth step whichdetermines the position of the input signal window based on the skipwidth calculated in the eighth step and sets the input signal window tothis position, and further, calculates the input signal similarity valuebased on each position of the input signal window by repeating the fifthstep to the ninth step, and determines whether or not the referencesignal exists at the position that the input signal window presentlyshows in the input signal based on the result of comparing the inputsignal similarity value and the predetermined threshold value.

According to another feature, the present invention provides a firststep that generates a feature quantity sequence for a plurality ofpre-recorded reference signals, a second step that sets the referencesignal window for each feature quantity sequence generated in the firststep, a third step that partitions the reference signal windows set inthe second step into a plurality of partitioned reference signalwindows, a fourth step that generates feature quantity sequence for theinput signal that has been input, a fifth step that sets the inputsignal window for the feature quantity sequences generated in the fourthstep, a sixth step that partitions this input signal window into aplurality of partitioned input signal windows corresponding to thisplurality of partitioned reference signal windows, a seventh stepcalculates an inter-reference similarity value that is a similarityvalue showing the degree of similarity between the feature quantitysequence related to a former reference signal and the feature quantitysequence related to a later reference signal for two reference signalsamong the plurality of reference signals, and is a similarity valueshowing the degree of similarity between the feature quantity sequencesof each partitioned reference signal window corresponding to each otherbetween these two reference signals, an eighth that step calculates theinput signal similarity value that shows the degree of similaritybetween the feature quantity sequence of each partitioned referencesignal window and the feature quantity sequence of the partitioned inputsignal window corresponding to thus partitioned reference signal windowfor each reference signal among the plurality of reference signals, aninth step that calculates a skip width indicating the amount that aninput signal window can move based on the inter-reference signalsimilarity value calculated in the seventh step and the input signalsimilarity value calculated in the eighth step, and a tenth step whichdetermines the position of the input signal window based on the skipwidth calculated in the ninth step and sets the input signal window atthat position, and further, calculates an input signal similarity valuefor each position of the input signal window by repeating the sixth stepto the tenth step, and determines whether or not the reference signalexists at the position that the input signal window presently shows inthe input signal based on the result of comparing the input signalsimilarity value and the predetermined threshold value.

According to another feature, the present invention provides a firststep that generates a feature quantity sequence of a plurality ofpre-recorded reference signals, a second step that sets a referencesignal window for each feature quantity sequence generated in the firststep, a third step that partitions the reference signal window set inthe second step into a plurality of partitioned reference signalwindows, a fourth step that generates a feature quantity sequence forthe input signal that has been input, a fifth step that obtains an inputsignal window for the feature quantity sequence generated in the fourthstep, a sixth step that partitions the input signal window into aplurality of partitioned input signal windows corresponding to theplurality of partitioned reference signal windows, a seventh step thatproduces a histogram of the feature quantity sequences of each of thepartitioned input signal windows, an eighth step that produces ahistogram of the feature quantity sequences of each of the partitionedinput signal windows, a ninth step that calculates an inter-referencesignal similarity value which is the similarity value showing the degreeof similarity between the histogram related to a later reference signaland a histogram related to a former reference signal for two referencesignals among this plurality of reference signals, and is the similarityvalue showing the degree of similarity between the histograms of eachpartitioned reference signal window corresponding to each other amongthe two inter-reference signals, a tenth step that calculates the inputsignal similarity value showing the degree of similarity between thehistogram of each of the partitioned reference signal windows and thehistogram of the partitioned input signal window corresponding to thispartitioned reference signal window for each reference signal among theplurality of reference signals, an eleventh step that calculates a skipwidth indicating the amount that the input signal window can move basedon the inter-reference signal similarity value calculated in the ninthstep and the input signal similarity value calculated in the tenth step,and twelfth step that determines the position of the input signal windowbased on the skip width calculated in the eleventh step and sets theinput signal window to that position, and further, calculates the inputsignal similarity value for each position of the input signal window byrepeating the sixth step to the twelfth step, and determines whether ornot the reference signal exists at the position that the input signalwindow presently shows in the input signal based on the result ofcomparing the input signal similarity value and the predeterminedthreshold value.

According to another feature, the present invention is a high-speedsignal search device that carries out each of the above steps.

According to another feature, the present invention is an automaticvideo control system providing the high-speed signal search device, avideo device, and a control means which controls the image movement ofthe video device based on the results of identification of thishigh-speed signal search device.

According to another feature, the present invention is a recordingmedium that records a program to execute each of the above steps.

According to the present invention, it is possible to obtain thedetection results identical to the case when precisely moving over thewhole area without precisely moving over the window for the entire areaof the input signal. In addition, according to the present invention, itis possible to search for the time of the appearance of specified musicor a commercial from among the signals over a long period of time of abroadcast, for example, and search for a signal including the specifiedsignal from a signal database, for example.

In addition, according to the present invention, for a feature quantitysequence of a plurality of windows, it is possible to carry out a searchtaking into account the before-and-after relationship between thesefeature quantity sequences (in the temporal axis). Thereby, even whenthe signal fluctuates due to noise, etc and when the signal is difficultto discriminate with average features, it is possible to detect thesignal with a higher precision. Moreover, as a setting configuration ofthe “plurality of windows”, for example, partitioning the originalwindow to make a plurality of windows that are set can be considered.

Furthermore, according to the present invention, it is possible todetect a signal by less processing that is conventional even whichdetecting a signal based on a plurality of reference signals.

Moreover, the program recorded on the recording medium of the presentinvention is read and executed by a computer, and thereby it is possibleto detect a signal with less processing than is conventional, and animprovement in the calculation efficiency in the signal detectionprocessing can be implemented.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block drawing showing an example of the structure of thehigh-speed signal search device according to the first embodiment of thepresent invention.

FIG. 2 is a flow chart showing an example of the operation of thehigh-speed signal search device according the first embodiment of thepresent invention.

FIG. 3 is a block diagram showing an example of the structure of thefeature extraction device 104.

FIG. 4 is an explanatory diagram showing an example of the similarityvalue calculation during the movement of the window.

FIG. 5 is a table showing an example of the detection precision of thehigh-speed device of the first embodiment of the present invention.

FIG. 6 is a table showing an example of the detection speed (the speedratio of the general hit method) of the high-speed signal search deviceaccording to the first embodiment of the present invention.

FIG. 7 is a block diagram showing an example of the structure of thehigh-speed signal search device according to a second embodiment of thepresent invention.

FIG. 8 is an explanatory drawing showing an example of the processing ofthe high-speed signal search device according to the second embodimentof the present invention.

FIG. 9 is a flowchart showing an example of the operation of thehigh-speed signal search apparatus according to the second embodiment ofthe present invention.

FIG. 10 is a block diagram showing an example of the structure forextracting a spectral feature.

FIG. 11 is a graph showing an example of the detection results for anaudio signal according to the conventional method.

FIG. 12 is a graph showing an example of the detection results for anaudio signal according to the second embodiment of the presentinvention.

FIG. 13 is a graph showing an example of the detection results for animage pattern according to the conventional method.

FIG. 14 is a graph showing an example of the detection results for animage pattern according to the second embodiment of the presentinvention.

FIG. 15 is a block diagram showing an example of the structure of thehigh-speed signal search device according to the third embodiment of thepresent invention.

FIG. 16 is a flow chart showing an example of the operation of thehigh-speed signal search device according to the third embodiment of thepresent invention.

FIG. 17 is a graph showing an example of the detection results for anaudio signal according to the third embodiment of the present invention.

FIG. 18 is a graph showing an example of the relationship between thesimilarity value between reference signals and the decrease in theeffect of the number of checks according to the third embodiment of thepresent invention.

PREFERRED EMBODIMENTS OF THE PRESENT INVENTION

The embodiments of the present invention will be explained referring tothe drawings.

First Embodiment

First, the first embodiment of the present invention will be explainedreferring to the figures. In the present invention, it is possible touse a variety of object signals for processing, but here, as one exampleof the processing of this object signal, an audio signal will be used.In addition, in the present invention, it is possible to use a varietyof feature quantities and a variety of degrees of similarity, but here,as one example of the feature quantities (for which the effect can beconsidered to be high) a histogram feature of a zero-crossing numberwill be used, and as an example of the degree of similarity, anintersection similarity value of normalized histograms will be used.

FIG. 1 is a block diagram showing an example of the structure of thehigh-speed signal search device applying the high-speed signal searchmethod according to the first embodiment of the present invention. Inaddition, FIG. 2 is a flow chart showing an example of the operation ofthe same. The present device comprises in general a feature templateproducing device 101 and an audio signal detection device 102.

Here, the high-speed signal search device shown in FIG. 1 specificallycomprises a computer made up of a CPU (central processing unit) and itsperipheral circuits. This computer carries out the functions of eachdevice shown in FIG. 1 by being controlled by a control program recordedon a specified recording medium (magnetic disc, semiconductor memory,etc.). Moreover, it is possible to distribute this computer program viaa communication circuit.

The feature template production device 101 is a device that produces adictionary template (that is, produces a feature quantity sequence ofthe reference audio signal) from a reference audio signal. Thedictionary template is used when detecting an input audio signal. Below,the each part of the feature template producing apparatus 101 isexplained. The audio signal input device 103 reads a reference audiosignal as learned data, and supplies the audio reference signal that hasbeen read to the feature extraction device 104. For example, if theprocessing subsequent to the feature extraction device 104 is carriedout digitally, the audio signal input device 103 comprises, for examplean A/D converter. The feature extraction device 104 calculates thefeature quantity (zero-crossing number) related to this audio signal(step S 101), and supplies this feature quantity to the histogramproduction device 105.

FIG. 3 is a block diagram showing an example of the structure of thefeature extraction device 104. In this figure, the frame partition unit113 partitions at a specified time interval (for example 11 msec) theaudio signal supplied by the audio signal input device 103, and suppliesthis partitioned audio signal to the zero-crossing calculator 114 andthe differentiator 115. Here, this specified time is called a “frame”.The zero-crossing calculator 114 calculates how many times the suppliedaudio signal (waveform) crosses the zero-level between these specifiedintervals, and outputs this calculation result as the output y1 of thefeature extraction device 104. Here, “crossing the zero-level” denotesthe changing of the signal level from a positive value to a non-positivevalue and the changing of the signal level from a non-positive value toa positive value.

The differentiator 115 differentiates the audio signal supplied from theframe partition unit 113, and supplies the result of differentiation tothe zero-crossing calculator 116 and the differentiator 117. Here, asone example of the differentiation, this can be carried out by takingthe differential between the neighboring sample values. Thezero-crossing calculator 116 has a structure equivalent to that of thezero-crossing calculator 114, calculates how many times the signalsupplied by the differentiator 115 crosses the above zero-level, andoutputs the result of the calculation as output y2 of the featureextraction device 104. The differentiator 117 has a structure equivalentto that of the differentiator 115, and integrates the signal suppliedfrom the differentiator 115, and supplies the result of this integrationto the zero-crossing calculator 118. The zero-crossing calculator 118has a structure equivalent to that of the zero-crossing calculator 114,calculates the number of times the signal supplied from thedifferentiator 117 crosses the zero-level during the above specifiedtime, and outputs the result of this calculation as the output y3 of thefeature extraction device 104. Below, the structure of the featureextraction device 104 is explained, but the structure is not limited tothe structure of this feature extraction device 104, and if the devicehas a function of extracting the features of an audio signal from theaudio signal supplied by the feature extraction device 104, anystructure is possible. The features can be expressed with any number ofgroups of feature quantities, such as, for example, y1, y2, and y3. Inthe present application, the grouping of several feature quantities inthis manner is called a “feature vector.”

The histogram production device 105 is a device that produces thehistogram of this feature vector from the sequence of feature vectorsfor each frame supplied from the feature extraction device 104 (step S102). The histogram of this feature vector classifies the feature vectorinto several patterns, and is produced by counting the number of featurevectors included in each classification. There are many methods ofclassifying the feature vectors into patterns that can be considered,but the histogram production device 105 partitions the range (fromminimum to maximum) of the values that the above feature vector can take(that is, the zero-crossing numbers y1, y2, y3 in each frame) into aplurality of sections, and by classifying the zero-crossings y1, y2, andy3 in each frame into any of the classifications among each of thesesections (according to its value), calculates the frequency of eachsection. Therefore, in the case of this example, a histogram g has anumber of bins that can be given by the sum of the number of sections ofeach feature quantity. In this manner, the learning stage is completedby producing a histogram of the reference audio signal. This histogramis supplied to the similarity value calculation device 110 as thetemplate for the reference audio signal.

The audio signal detection device 102 detects in the input audio signalsequence the position of the audio signal that is similar to thereference audio signal. The following is a concrete explanation of theaudio signal detection device 102.

The audio signal input device 106 is a device for reading an input audiosignal, and has a structure equivalent to that of the audio signal inputdevice 103. The audio signal read from the audio signal input device 106is supplied to the window region scan extracting device 107. The windowregion scan extracting device 107 uses the input signal window from theaudio signal supplied from the audio signal input device 106, extractsthe audio signal in the area of interest that this input signal windowshows (step S 103), and supplies this extracted audio signal to thefeature extraction device 108. The window region scan extracting device107 extracts the audio signal while sequentially moving the position ofthis input signal window (that is, the area of interest), and the skipwidth is calculated by the upper bound calculation device 111. Moreover,while the input sequence window is being sequentially moved, the size ofthis input signal window is constant.

The feature extraction device 108 has a structure equivalent to that ofthe feature extraction device 104, and an example of this structure isshown in the FIG. 3. The feature extraction device 108 calculates thefeature quantity related to the audio signal based on the audio signalextracted by the window region scan extracting device 107 (step S 104).The output of the feature extraction device 108 is supplied to thehistogram production device 109. The histogram production device 109 hasa structure equivalent to that of the histogram production device 105,and produces a histogram h for the feature quantities (that is, thezero-crossing numbers y1, y2, and y3) in each frame supplied by thefeature extraction device 108 by calculating the frequency of thezero-crossing numbers (step S 105), and supplies this histogram h to thesimilarity value calculation device 110.

The similarity value calculation device 110 calculates the similarityvalue between the normalized histogram h supplied from the histogramproduction device 109 and the normalized histogram g supplied by thefeature template production device 101 (step S 106). Many definitions ofa similarity value are possible, but here, as one example, theintersection similarity value shown in the following equation is used:$\begin{matrix}{{S\left( {g,h} \right)} = {\frac{1}{D}{\sum\limits_{j = 1}^{L}{\min\left( {g_{j},h_{j}} \right)}}}} & (1)\end{matrix}$Here, D is the total frequency of the histogram, L is the number ofhistogram bins, g_(i) is the value of the j^(th) bin of the histogram g,h_(j) is the value of the j^(th) bin of histogram h, and min (g_(j),h_(j)) is the smaller value between g_(j) and h_(j). The calculatedsimilarity value is supplied to the upper bound calculation device 111and the detection determination device 112.

Moreover, a degree of similarity that shows the degree of correlationbetween histograms is not limited to the above intersection similarityvalue, but for example, can use a value corresponding to the distancebetween histograms. Here, the “value corresponding to the distancebetween histograms is, for example, the value totaling over all bins theabsolute value of the difference of frequencies of each bincorresponding to each other in the normalized histograms g and h(Σ|g_(j)−h_(j)).

The upper bound calculation device 111 calculates the upper bound of thesimilarity value at each point for each point for the neighboring pointsthat have a similarity value based on the similarity value alreadyobtained. For example, in the case of the intersection similarity valueof the histograms, as shown in FIG. 4, the difference between thehistogram of window A and the histogram of window B (set correspondingto the time series of the input audio signal) depends only on the numberof samples included in the region not common between window A and windowB. That is, like the intersection similarity value, in the normalizedhistogram, when the similarity value is determined based on the resultsof the comparison for each bin found for the frame units accumulated forall bins, the change in the similarity value produced as the inputsignal window moves is necessarily limited to the proportion that thenumber of samples not in common between input signal windows occupieswith respect to the total number of samples (in this case, the number offrames in the input signal window) before and after moving. If thisquality is used, the similarity value between the feature quantities ofthe input audio signal within a given input signal window and thefeature quantity of a give reference signal will become limited to beinglower than a specified upper bound (in the case of distance, above alower bound) by the similarity value in the neighborhood of the inputsignal window (the feature quantity of the input audio signal in aninput signal window and the feature quantity of a given referencesignal).

As an example, consider the case of finding a region wherein thesimilarity value is above a given threshold value. In this case, whenfinding (carrying out checking) a similarity value between a featurehistogram of the input audio signal at a given time and the histogramproduced by the feature template production device 101, the upper boundof the similarity value at the point in the input audio signal in theneighborhood of this time can be found by calculation, not by actuallyfinding (carrying out checking) the similarity value of the histogram ofthe feature template at these points in time. That is, in the binwherein the upper bound of the similarity value is below a thresholdvalue, it can be omitted because the operation of finding the similarityvalue is not necessary. For example, in the case of this example, if S(step S 106 in FIG. 2, and process 31 in FIG. 4) is the similarity valuefound for the histogram h for the input audio signal at a given time andthe histogram g produced by the feature template production device 101,θ is the threshold value of the similarity value being sought, and D isthe number of frames in the input signal window, then when S<θ, theamount the signal input window can move is calculated as: floor {D(θ−S)}+1 frame (step S 107 of FIG. 2, and process 32 in FIG. 4). Here,floor { } denotes the cutoff for rounding off the number. This skipwidth is supplied by the window region scan extracting device 107. Thewindow region scan extracting device 107 moves the input signal windowbased on this skip width (step S 108).

Moreover, in this processing, it is very important to set the thresholdvalue θ at an appropriate value. However, because the appropriatethreshold value θ depends on the input audio signal and the referenceaudio signal, and the search parameters, it is not advantageous to makethis threshold value a single fixed value. Thus, here the thresholdvalue θ is set at θ=m+cv, based on mean m and variation v of thesimilarity value when the search was carried out in the direction of thetime axis. Here, m and v are set by sampling the feature vector of theinput audio signal preceding the search and taking the statistics of thesimilarity value. In addition, c is a specified parameter.

In this manner, the present device moves in sequence the input signalwindow, and the same process is repeated until the input signal windowpasses the end of the of the input audio signal. At this time, if thesimilarity value supplied by the similarity value calculation device 110exceeds the above threshold value θ, the detection determination device112 determines that the audio signal in the input signal window isequivalent to the reference audio signal, and outputs the position (thatis, the generation time of this audio signal) of this input signalwindow as the result (step S 109). By the above processing, it ispossible to detect the position (generation time) of the reference audiosignal from the input audio signal.

Using the present embodiment, an experiment was carried out wherein, apart that is similar to a pre-recorded reference audio signal wasdetected in two-minute input audio signals actually recorded from atelevision broadcast. Moreover, the reference audio signals used as therecorded data were part of an input audio signal recorded separately andinput. The reference audio signals had a length of 5.94 seconds and11.89 seconds, and the experiment was carried out using audio signalsthat had 109 differences. The detection precision is shown in FIG. 5,and the comparison of the number of times of checking with the techniqueof checking while shifting the total hits is shown in FIG. 6. Moreover,in FIG. 5, the recall factor and precision are calculated by thefollowing equations:recall factor=(number of correct detection)/(number of correctlocations)precision=(number of correct detection)/(total number detected)Second Embodiment

Next, the second embodiment of the present invention will be explainedreferring to the figures.

In the first embodiment, as a method for detecting the position of asignal similar to a pre-recorded reference signal, the method ofcalculating the feature quantities of a reference signal and the inputsignal, and identifying the similarity value of both was explained.Further, in the first embodiment, a method was explained wherein thesearch speed was increased by decreasing the amount of calculation bycalculating a similarity value of the feature quantities of thereference signal and the input signal using a specified time of thewindow (region of interest) as the unit, and determining the amount ofmovement of the window based on the upper bound of the similarity valuein the neighboring area of interest of this area of interest for whichthe similarity value calculation was made.

However, in the method of the first embodiment, there are the problemsthat when the signal fluctuates due to noise, etc., and the signal isdifficult to discriminate using average features (for example, the audiosignal of an announcement), the signal cannot be detected adequately.

Thus, in the second embodiment, a high-speed signal search method isexplained wherein it is possible to detect the signal at a higherprecision even when the signal fluctuates due to noise, etc., and whenthe signal is difficult to discriminate with average features.

FIG. 7 is a block diagram showing an example of the structure of thehigh-speed signal search device applying the high-speed signal searchmethod according to the second embodiment of the present invention. Inthe present invention, it is possible to use a variety of object signalsfor processing, but here, as one example of the processing of thisobject signal, an audio signal will be used. The present devicecomprises the reference feature quantity extraction means 201, the inputfeature quantity extraction means 202, the similarity value calculationmeans 203, and the movement quantity calculation means 204. The presentdevice inputs a pre-recorded reference audio signal (that is, the sampleof the audio signal to be searched for) and an input audio signal (thatis, the audio signal that is searched for), and detects locationswherein the similarity value of the reference audio signal and the inputaudio signal exceeds a specified threshold value θ.

Here, the high-speed signal search device shown in FIG. 7 is concretelystructured from a computer comprising a CPU (central processing unit)and its peripheral circuits. This computer carries out the function ofeach device shown in FIG. 7 by being controlled by a control programrecorded on a specified recording medium (magnetic disc, semiconductormemory, etc.). Moreover, it is possible to distribute this computerprogram via a telecommunication circuit.

In FIG. 7, the reference feature quantity extraction means 201 sets aplurality of windows (referred to hereinbelow as the “reference signalwindows”) based on the feature quantity sequences generated from thereference audio signal, partitions this reference signal window into aplurality of windows (referred to hereinbelow as the “partitionedreference signal windows”), and supplies this feature quantity sequencein each partitioned reference signal window to the similarity valuecalculation means 203. The input feature quantity extraction means 202sets the window (referred to hereinbelow as the “input signal window”)based on the feature quantity sequences generated from the input audiosignal, and partitions this input signal window into a plurality ofwindows (referred to hereinbelow as the “partitioned input signalwindows”), and supplies the feature quantity sequence in each of thepartitioned input signal windows to the similarity value calculationmeans 203.

The similarity value calculation means 203 calculates a similarity value(referred to hereinbelow as the “input signal similarity value”) betweenthe feature quantity sequences of each reference input partitionedwindow and the feature quantity sequences in each partitioned inputsignal window (referred to hereinbelow as the “input signal similarityvalue”). Additionally, the similarity value calculation means 203determines whether there is a reference audio signal at the positionthat the input signal window presently shows based on whether thecalculated input signal similarity value is greater than a thresholdvalue θ. When there is a reference audio signal at the position theinput signal window presently shows, the similarity value calculationmeans 203 outputs the position (time, etc.) that the input signal windowpresently shows as the result of the signal detection. In addition, thesimilarity value calculation means 203 supplies the similarity valuenecessary to calculate the skip width of the input signal window andsupplies it to the movement quantity calculation means 204.

The movement quantity calculation means 204 calculates the upper boundof the similarity value related to the neighboring input signal windowof the input signal window corresponding to this similarity value basedon the similarity value supplied from the similarity value calculationmeans 203. Additionally, the movement quantity calculation means 204calculates the skip width of the input signal window based on this upperbound, and supplies this skip width to the input feature quantityextraction means 202. Thereby, the input feature quantity extractionmeans 202 moves the position of the input signal window only the skipwidth supplied from the movement quantity calculation means 204. In thismanner, the present invention moves in sequence the input signal window,and repeats in the same manner the each of the above processes for thefeature quantity sequences generated in the input audio signal of theinput signal window after being moved.

Next, the operation of the present device will be concretely explainedreferring to FIG. 8 and FIG. 9.

The reference feature quantity extraction means 201 first reads thepre-recorded reference audio signal. Next, the reference featurequantity extraction means 201 carries out extraction of the featurequantities for the reference audio signal which was read (stepS 201). Inthe present invention, it is possible to use a variety of featurequantities, but here, as one example of the feature quantity, a spectralfeature will be used. In this case, the extraction of the featurequantity can be carried out using, for example, a band-pass filter.

A concrete example of the feature quality extraction will be explainedbelow. For example, when a specified audio signal of about 10 seconds isto be searched for in a broadcast signal for television or radio, etc.,when the feature quantities are extracted with the device shown in FIG.10, good results can be obtained. That is, 7 band-pass filters are setso that the central frequencies of these band-pass filters have equalintervals on a logarithmic axis, a 60 msec time window is set for theoutput waveform of each band-pass filter, and the mean value of thesquare of the output waveform in this time window is calculated. The 7average values obtained in this manner are grouped into a 7-dimensionalfeature vector. While this time window is shifted by 12 msec units, thecalculation of the sequence feature vector is carried out. In this case,one feature vector at a time is obtained every 12 msec. In the referencefeature quantity extraction means 201, in this manner, the featurevector having the component of each frequency band of the referenceaudio signal an element is obtained in a sequential time series.Moreover, the following method can be considered. That is, in thismethod, the 7-dimension vector is obtained by setting 7 band-passfilters so that the central frequencies of these band-pass filters haveequal intervals on a logarithmic axis, a 60 msec time window is set forthe output waveform of each band-pass filter, the difference between theread output and the prior read output is calculated, the average valueof the square of the output waveform is calculated, and the 7 averagevalues are grouped to make a 7-dimensional feature vector.

Next, the reference feature quantity extraction means 201 sets thewindow for the time series of these feature vectors (step S 202). Thereference feature quantity extraction means 201 first sets one window(reference signal window) for the entire reference audio signal (referto “window” in the reference feature quantity extraction means 201 inFIG. 8). Moreover, FIG. 8 schematically shows that the horizontaldirection in the reference signal window corresponds to the time axis,and the feature vectors are obtained in sequence in the direction ofthis time axis.

Next, the reference feature quantity extraction means 201 partitions thereference signal window into a plurality of partitioned reference signalwindows in the direction of the time axis (step S 203; refer to“partition” in the reference feature quantity extraction means 201 ofFIG. 8). Moreover, the number of partitions is appropriately determinedaccording to the condition of the signal which is the object ofprocessing, etc., but here, for example, there are 8 partitions.Thereby, the reference feature quantity extraction means 201 supplies insequence the feature quantities (that is, the time series of the featurevector) included in each partitioned reference signal window to thesimilarity value calculation means 203.

The input feature quantity extraction means 202 first reads the inputaudio signal. Next, the input feature quantity extraction means 202carries out detection of the feature quantities for the read input audiosignal (step S 204). Here, the input feature quantity extraction means202 extracts the feature quantity (that is, the time series of the7-dimensional vector) by the same method (refer to FIG. 10) as thereference feature quantity extraction means 201. Thus, here, theexplanation of this will be omitted.

Next, the input feature quantity extraction means 202 sets the windowsfor the time series of these feature vectors (step S 205). The inputfeature quantity extraction means 202 first sets the length of thewindows (the input signal windows) for the input audio signal so as tobe the same as those of the above reference signal windows (see“windows” in the input feature quantity extraction means 202 in FIG. 8).Moreover, FIG. 8 shows schematically that the horizontal direction ofthe input signal window corresponds to the time axis, and the featurevectors are obtained in sequence in the direction of this time axis.

Moreover, at the commencement of the processing, the position of theinput signal window is set at the head of the feature quality sequenceproduced from the input audio signal, but as the processing proceeds, bya method described below, this feature quantity is moved in sequence inthe direction of the time axis. The amount of this movement iscalculated by the movement quantity calculation means 204.

Next, the input feature quantity extraction means 202 partitions theinput signal window into a plurality of partitioned input signal windowsin the direction of the time axis (step S 206; see “partition” in theinput feature quantity extraction means 202 in FIG. 8). Moreover, thenumber of partitions of the input signal window is equal to the numberof partitions of the reference signal window. Thereby, the input featurequantity extraction means 202 outputs to the similarity valuecalculation means 203 in sequence the feature quantities (that is, thetime series of the feature vector) included in each partitioned inputsignal window.

The similarity value calculation means 203 reads in sequence the timeseries of the feature vector partitioned in the partitioned referencesignal window, and reads in sequence the time series of the featurevector partitioned in the partitioned input signal window from the inputfeature quantity extraction means 202.

Next, the similarity value calculation means 203 produces a histogram ofthe feature vector based on the time series of the feature vector of thepartitioned reference signal window (step S 207), and produces thehistogram of the feature vector based on the time series of the featurevector in the partitioned input signal window (step S 208). Thesehistograms are all produced by partitioning the range (from the smallestvalue to the largest value) of the values that each element of thefeature vector can take into a plurality of bins (sections). Forexample, the range of values that each of the elements can take (here,assumed to be “bins A, B, C”) are partitioned into three bins, and ifthe number of elements of each feature vector is 7, then the 7 elementsare respectively distributed among the bins A, B, and C. Therefore, whenconsidering one feature vector, as a combination of the elements of thisfeature vector from (A, A, A, A, A, A, A) showing all the 7 elementsincluded in bin A to the (C, C, C, C, C, C, C) showing all the 7elements included in bin C, a total combination of 3 to the power of 7can be conceived. From the above, the total number of bins (that is, thenumber of bins disposed on the horizontal axis of the histogram) of thehistograms of the feature vectors is 3 to the power of 7. Therefore,when setting the horizontal axis of the histogram in this manner, eachfeature vector is classified into one among these 3 to the power of 7number of bins.

By the above described method, the similarity value calculation means203 produces the respective histograms for the feature vectors(partitioned in the direction of the time axis) supplied from thereference feature quantity extraction means 201 and the feature vectors(partitioned in the direction of the time axis) supplied from the inputfeature quantity extraction means 202. Here, these histograms are G¹,G², . . . , G^(a) nd H¹, H², . . . , H^(n), where n (1˜n is referred tohereinbelow as the “number of partitions”) is the number of partitionsof the original windows (reference signal window and input signalwindow), G denotes the histogram (referred to hereinbelow as the“reference signal histogram”), and H denotes the histogram (referred tohereinbelow as the “input signal histogram”) produced from the featurevector of the input audio signal.

Next, the similarity value calculation means 203 calculates thesimilarity value between the reference signal histogram and the inputsignal histogram having an equal number of partitions (step S 209). Inthe present invention, it is possible to use various similarity values,but here the intersection similarity value is used as an example of thissimilarity value. Here, the similarity value S^(k) (of the referencesignal histogram and the input signal histogram) in the partition numberk is defined in the following equation: $\begin{matrix}{S_{k} = {\frac{1}{D_{k}}{\sum\limits_{j = 1}^{L}{\min\left( {g_{j}^{k},h_{j}^{k}} \right)}}}} & (2)\end{matrix}$Here, D_(k) denotes the total number of histograms of the partitionnumber k, L denotes the number of histogram bins (in the above example,3 to the power of 7), g^(k) _(j) denotes the value of the j^(th) bin ofthe k^(th) histogram G_(k), h^(k) _(j) denotes the value of the j^(th)bin of the k^(th) histogram H^(k), and min (g^(k) _(j), h^(k) _(j))denotes the smaller between g^(k) _(j) and h^(k) _(j).

In addition, the similarity value S (that is, the similarity valuebetween the reference signal window and the input signal window) for allwindows is defined by the following equation (below, this similarityvalue S is referred to as the “total similarity value”).S=min(S ₁ , S ₂ , . . . S _(n))  (3)Here, min (S₁, S₂, . . . , S_(n)) denotes the minimum value among S₁,S₂, . . . , S_(n).

The calculation of the similarity value S^(k) is carried out one at atime (for example, in the order from the smallest k). If the calculatedsimilarity value S^(k) is below the threshold value θ, based on Eq. 3,the minimum value of the total similarity value S is clearly below thethreshold value θ, and thus it is not necessary to carry out thecalculation of the subsequent similarity values Sk+1, Sk+2, Sk+3, . . .. Additionally, the movement quantity calculation means 204 supplies thesmallest value among the similarity values calculated up to this pointto the movement quantity calculation means 204.

In contrast, if all of the similarity values S₁˜S_(n) are larger thanthe threshold value θ, then the total similarity value S will be largerthan the threshold value θ. This means that at the present position ofthe input signal window for the (feature vector of the) input audiosignal the reference audio signal has been detected. Thus, thesimilarity value calculation means 203 outputs this present position(time) as part of the signal detection results. In addition, in thiscase as well, the similarity value calculation means 203 supplies thetotal similarity value S to the movement quantity calculation means 204.

The movement quantity calculation means 204 first reads the totalsimilarity value S supplied from the similarity value calculation means203. Next, the movement quantity calculation means 204 calculates theskip width w (step S 210). Here, the skip width w is found by thefollowing equation: $\begin{matrix}{w = \left\{ \begin{matrix}{{{floor}\quad\left( {D\left( {\theta - S} \right)} \right)} + 1} & \left( {S < \theta} \right) \\1 & ({otherwise})\end{matrix} \right.} & (4)\end{matrix}$Here, the unit of the skip width w is the number of feature vectors,floor ( ) denotes the cutoff for rounding off the number, D is the totalfrequency of the histogram of the feature vector in the k^(th)partitioned window (the partitioned reference signal window and thepartitioned input signal window) satisfying S=S_(k), and θ denotes theabove-described threshold value.

Eq. 4 means that if S<θ at the present point, even if the input signalwindow is moved at least one feature vector interval (w−1), the totalsimilarity value S will not exceed the threshold value θ. This can beeasily understood by considering the case where the feature vector thatis outside the input signal window is contributing nothing to theoverlap of all histograms when the input signal window is moving and thecase where the feature vector within the input signal window iscontributing all of the overlap to histograms (that is, when the totalsimilarity value S arrives most quickly at the threshold value θ). Thatis, because in this kind of case the similarity value increases themost, under this supposition, when the input signal window is moved atleast the feature vector interval (w−1), the upper bound of the totalsimilarity value S becomes the threshold value θ. Due to this, if S<θ,then the amount of movement of the feature vector w for which the totalsimilarity value S may exceed the threshold value θ is made the skipwidth.

In contrast, if S≧θ, in order to find the local peak of the similarityvalue, then w=1, and a search is carried out that does not skip anywindows.

The skip width w output from the movement quantity calculation means 204is supplied to the input feature quantity extraction means 202. Theinput feature quantity extraction means 202 moves the input signalwindow only one feature vector interval w (step S 211; refer to thewindow with the broken line in the input feature quantity extractionmeans 202 in FIG. 8). Subsequently, the above-described processing(setting a plurality of partitioned windows, producing histograms,calculating similarity values, calculating the skip width, etc.) arerepeated in the same manner (step S 212). In addition, by moving theinput signal window, when the input signal window passes the end of theinput audio signal (of the time series of the feature vector), thesearch processing is ended because the entire input audio signal hasbeen searched.

Next, an example of an operational experiment of the high-speed signalsearch device according to the present embodiment will be given. Thepresent apparatus is mounted on a work station (SGI O₂), and FIG. 11 andFIG. 12 show the results of the detection of an audio signal using atelevision broadcast as the subject matter. In both figures, thevertical axis is the similarity value, and the horizontal axis shows thetime. A commercial (15 seconds) was used as the reference signal, andthe detection was carried out using an the recording of an actualtelevision broadcast (Jan. 22, 1998, from 18:22 to 00:22) as an inputsignal. Moreover, in this experiment, the sampling frequency was 11.025kHz, the dimension of the feature vector was 7, an the number of bins ofthe elements of each feature vector was 3.

FIG. 11 shows the case when the window was not partitioned, that is,when the present method was not applied, and FIG. 12 shows the case whenthe window was partitioned into 8 sections by applying the presentmethod. In the respective figures, the parts with the mark ‘o’ denotelocations found the present method. Manual confirmation showed thatthere were three correct locations in the present experiment:

-   -   18:48, 23:22, 00:11.        In FIG. 11, three extraneous locations were detected, and in        FIG. 12, the correct detection results were obtained.

In addition, generally the larger the ratio of the similarity value inlocations that should be searched and the similarity value of locationsthat should not be searched, the margin with respect to setting thesetting values becomes large, and thus a stable search is possible. Inthe present experiment, for the case shown in FIG. 12 the value of thisratio has become large in comparison to the case shown if FIG. 11, andthe effect of partitioning the windows clearly appears.

In addition, the results of signal detection for an image pattern areshown in FIG. 13 and FIG. 14. In both cases, the vertical axis is thesimilarity value and the horizontal axis shows the time. In these cases,the detection is carried out using as the reference signal colorinformation obtained from an image of a commercial (15 sec) differentfrom that shown in FIG. 11 and FIG. 12, and using as the input signalcolor information obtained by recording an actual broadcast (Jan. 22,1998, from 18:22 to 18:26). Moreover, in this experiment the dimensionof the feature vector was 3 and the number of bins of the elements ofeach feature vector was 8.

FIG. 13 shows the case when the window is not partitioned (that is, thecase when the present method is not applied), and FIG. 14 shows the casewhen the window is partitioned into 8 sections by applying the presentmethod. In the respective figures, the parts with the mark ‘o’ denotelocations found by the present method. Manual confirmation showed thatthere were four correct locations in the present experiment:

-   -   18:48, 19:56, 20:35, 22:54.        In FIG. 13, one location among the correct locations (20:35) was        not detected, and two extraneous locations were detected, while        in contrast, in FIG. 14, the correct detection results were        obtained.        Third Embodiment

Next, the third embodiment of the present invention will be explainedreferring to the figures.

In the first embodiment and the second embodiment, methods wereexplained wherein the position in a signal similar to a pre-recordedreference signal was detected.

However, in these methods, there is the problem that when detecting asignal based on a plurality of reference signals, the number of timesthat the detection processing is repeated is equal to at least thenumber of reference signals, the amount of calculation increased, andthe detection speed decreased.

Thus, in the third embodiment, a high-speed signal search method will beexplained wherein it is possible to detect a signal with less processingthan conventionally even when the signal is detected based on aplurality of reference signals.

FIG. 15 is a block diagram showing an example of the structure of thehigh-speed signal search device applying the high-speed signal searchmethod according the third embodiment of the present invention. In thepresent invention, it is possible to use a variety of object signals forprocessing, but here, as one example of the processing of this objectsignal, an audio signal will be used. In FIG. 15, the present device 1comprises the reference feature quantity calculation means 310, theinput feature quantity calculation means 312, the inter-reference signalsimilarity value calculation means 314, the similarity value calculationmeans 316, and the skip width calculation means 318.

Here, the high-speed signal search device shown in FIG. 15 specificallycomprises a computer made up of a CPU (central processing unit) and itsperipheral circuits. This computer carries out the functions of eachdevice shown in FIG. 15 by being controlled by a control programrecorded on a specified recording medium (magnetic disc, semiconductormemory, etc.). Moreover, it is possible to distribute this computerprogram via a communication circuit.

The present device 1 uses a pre-recorded reference signal (that is, thesample audio signal to be detected) and the input audio signal (that isthe audio signal to be detected) as input, and detects from the inputaudio signal locations where the similarity value of the reference audiosignal and the input audio signal exceeds a specified threshold value θ.

The reference feature quantity calculation means 310 generates a featurequantity sequence for a plurality of reference audio signals. Inaddition the input feature quantity calculation means 312 generates afeature quantity series from the input audio signal, and sets the window(hereinbelow, referred to at the “input signal window”) based on thisfeature quantity sequence.

The inter-reference signal similarity value calculation means 314calculates the similarity value between each (feature quantity sequenceof) the reference audio signal (hereinbelow, referred to as “theinter-reference signal similarity value”). The similarity valuecalculation means 316 calculates the similarity value (hereinbelow,referred to as the “input signal similarity value”) of the featurequantity sequence generated by the reference feature quantitycalculation means 310 and the feature quantity sequence in the inputsignal window set by the input feature quantity calculation means 312.

The skip width calculation means 318 calculates a parameter (forexample, the upper bound of the similarity value) that defines the rangeof the input signal value (between the input audio signal and thereference audio signal) in the input signal window in the neighborhoodof the input signal window corresponding to this input signal similarityvalue based on the inter-reference signal similarity value calculated bythe inter-reference signal similarity value calculation means 314 andthe input signal similarity value calculated by the similarity valuecalculation means 316. Additionally, the skip width calculation means318 calculates the skip width of the input signal window based on thevalue of this parameter.

Next, referring to FIG. 16, the operation of the present device will beconcretely explained. In order to simplify the explanation, the lengthof all the reference audio signals are equal. Moreover, actually thepresent device can be applied even when length of the reference audiosignals are not necessarily the same.

The reference feature quantity calculation means 310 first reads all thegiven reference signals. Next, the reference feature quantitycalculation means 310 carries out extraction of the feature quantitiesfor the reference audio signals that have been read (step S 301). In thepresent invention, it is possible to use a variety of featurequantities, but here, as one example of the feature quantity, a spectralfeature will be used. In this case, the reference feature quantitycalculation means 310 extracts the feature quantities (that is, the timeseries of the 7-dimensional vector) using the same method (see FIG. 10)as the reference feature quantity extraction means 201 of the secondembodiment, and thus, here its explanation will be omitted.

Next, the reference feature quantity calculation means 310 produceshistograms of these feature vectors based on the time sequence of theabove feature vector (step S 302). Here, this histogram is produced bypartitioning into a plurality of bins the range of values (from thesmallest value to the largest value) that each element of the featurevector can take. The reference feature quantity calculation means 310produces the histograms using the same method as the similarity valuecalculation means 203 (second embodiment), and thus here its explanationis omitted.

Moreover, in the present embodiment, a histogram is produced for all(the time sequence of the feature vector) of one reference audio signalbut as shown in the second embodiment, it is alternatively possiblepartition the (time sequence of the feature vector of) the referenceaudio signal, and produce histograms for each of (the time sequence ofthe feature vector of) the reference audio signals after partitioning.There can be, for example, 4 partitions. In this case, 4 histograms areproduced for one reference audio signal.

The input feature quantity calculation means 312 first reads the inputaudio signal. Next, the input feature quantity calculation means 312carries out extraction of the feature quantities for the input audiosignal that has been read (step S 303). Here the input feature quantitycalculation means 312 extracts the feature quantities using the samemethod as the reference feature quantity calculation means 310.

Next, the input feature quantity calculation means 312 sets the lengthof the input signal window so as to be the same as that of the referenceaudio signal provided by the reference feature quantity calculationmeans 310 for (the time series of the feature vector of) the extractedfeature quantities (step S 304). Moreover, at the beginning of theprocessing, the position of the input signal window is set at the headof the feature quantity sequence produced from the input audio signal,but as the processing progresses, by a method described below, thisfeature quantity sequence is moved sequentially in the direction of thetime axis. This skip width is calculated by the similarity valuecalculation means 316.

Next, the input feature quantity calculation means 312 produceshistograms for these feature vectors based on the time series of thefeature vectors in the input signal window (step S 305). Here, thishistogram is produced by partitioning the range of values (from thesmallest to the largest) that each element of the feature vector cantake into a plurality of bins, the input feature quantity calculationmeans 312 produces the histograms using the same method as the referencefeature quantity calculation means 310, and thus, its explanation isomitted here.

Moreover, when the reference feature quantity calculation means 310partitions the reference audio signal (as in the second embodiment) inthe direction of the time axis, and the input feature quantitycalculation means 312 also, in the same manner, partitions (the timeseries of the feature quantity in) the input signal window in thedirection of the time axis. The number of partitions is the same as thenumber of partitions in the reference feature quantity calculation means310. Therefore, in the input feature quantity calculation means 312, thesame number of histograms as were produced from one reference audiosignal in the reference feature quantity calculation means 310 isproduced from the input audio signal in the input signal window.

The inter-reference signal similarity value calculation means 314 firstreads the histograms (the histograms of each of the reference audiosignals) supplied from the reference feature quantity calculation means310. Here, in order to simplify the explanation, the case is consideredwherein the reference audio signal is not partitioned (however, this isonly to prevent the treatment of the subscripts from becomingcomplicated, and the present invention can be applied to the casewherein the reference audio signal is partitioned). The histogramssupplied from the reference feature quantity calculation means 310 areG¹, G², . . . G^(N), where N is the number of reference audio signals.

The inter-reference signal similarity value calculation means 314calculates the similarity values between two extracted reference audiosignals for all combinations of two reference audio signals from among Nreference audio signals (step S 306). Here, the similarity value S (g,h) between the histogram g of the k^(th) reference audio signal and thehistogram h of the m^(th) reference audio signal is defined by thefollowing equation: $\begin{matrix}{{S\left( {g,h} \right)} = {\frac{1}{D}{\sum\limits_{j = 1}^{L}{\min\left( {g_{j},h_{j}} \right)}}}} & (5)\end{matrix}$Here, D is the total number of histograms, L is the number of histogrambins (in the above example, 3 to the power of 7), g_(j) is the value ofthe j^(th) bin of histogram g, h_(j) is the value of the j^(th) bin ofhistogram h, and min (g_(j), h_(j)) is the smaller value between g_(j)and h_(j).

Moreover, when the reference feature quantity calculation means 310partitions the reference audio signal in the direction of the time axis(as in the second embodiment), the inter-reference signal similarityvalue calculation means 314 carries out the calculation of thesimilarity values for the partitioned parts corresponding to the tworeference audio signals. The result of the calculation of the similarityvalues is stored in a memory means (not shown in the figures), andsupplied to the skip width calculation means 318.

The similarity value calculation means 316 first reads the histogramsupplied by the reference feature quantity calculation means 310 and thehistogram supplied by the input feature quantity calculation means 312.Here, in order to simplify the explanation, the case when the referenceaudio signal and the input audio signal are not partitioned isconsidered (however, this is only to prevent the treatment of thesubscripts from becoming complicated, and the present invention can beapplied even in the case that the reference audio signal and the inputaudio signal are partitioned). Here, the histograms supplied from thereference feature quantity calculation means 310 are G₁, G₂, . . . ,G_(N), where N is the number of reference audio signals, and thehistogram supplied from the input feature quantity calculation means 312is H.

Next, the similarity value calculation means 316 selects one histogramof a reference audio signal, and calculates the similarity value betweenthe selected histogram and the histogram of the input audio signal (stepS 307). The similarity value S(g, h) between the histogram g of thereference audio signal and the histogram h of the input audio signal isdefined b the following equation: $\begin{matrix}{{S\left( {g,h} \right)} = {\frac{1}{D}{\sum\limits_{j = 1}^{L}{\min\left( {g_{j},h_{j}} \right)}}}} & (6)\end{matrix}$Here, D is the total number of histograms, L is the number of histogrambins (in the above example, 3 to the power of 7), g_(j) is the value ofthe j^(th) bin of histogram g, h_(j) is the value of the j^(th) bin ofhistogram h, and min (g_(j), h_(j)) is the smaller value between g_(j),and h_(j).

In the present embodiment, N reference audio signals are input, but thecalculation of the similarity value is carried out once for only one ofthe reference audio signals. The reference audio signal selected for usein the calculation of the similarity value will have a similarity valuewhose upper bound is expected to exceed a threshold value θ. Because theskip width calculation means 318 explained below determines which of thereference audio signals fulfills this criterion, the similarity valuecalculation means 316 obtains this information from the skip widthcalculation means 318.

Moreover, in the case that the reference audio signal and the inputaudio signal are partitioned in the direction of the time axis (as inthe second embodiment), the calculation of the similarity value iscarried out for one partitioned part of the (partitioned) referenceaudio signal and input audio signal.

The similarity value calculation means 316 supplies the calculatedsimilarity values to the skip width calculation means 318. In addition,when the similarity value exceeds the threshold value θ (the case thatthe reference audio signal and the input audio signal are partitioned inthe direction of the time axis, and when for all partitions, thesimilarity value exceeds the threshold value θ), because this means thatthe reference audio signal has been found in the input audio signal, thesimilarity value calculation means 316 outputs the number of thereference audio signal and the present position of the input signalwindow.

The skip width calculation means 318 first reads from the similarityvalue calculation means 316 the similarity value S^(k) between the inputaudio signal and the k^(th) reference audio signal. Next, the skip widthcalculation means 318 calculates the skip width w_(k) (step S 308).Here, the skip width w_(k) is found with the following equation:$\begin{matrix}{w_{k} = \left\{ \begin{matrix}{{{floor}\quad\left( {D\left( {\theta - S^{k}} \right)} \right)} + 1} & \left( {S^{k} < \theta} \right) \\1 & ({otherwise})\end{matrix} \right.} & (7)\end{matrix}$Here, the unit of the skip width w_(k) is the number of feature vectors,floor ( ) denotes the cutoff for rounding off the number, D is the totalnumber of histograms of the feature vectors of the k^(th) referenceaudio signal that satisfies S=S^(k), and θ is the above threshold value.

Eq. 7 means that if S<θ at the present point, even if the input signalwindow is moved at least one feature vector interval (w_(k)−1), thetotal similarity value S^(k) will not exceed the threshold value θ. Thiscan be easily understood by considering the cases where the featurevector that is outside the input signal window is contributing nothingto the overlap of all histograms when the input signal window is movingand the feature vector within the input signal window is contributingall of the overlap to histograms (that is, when the total similarityvalue S^(k) arrives most quickly at the threshold value θ). That is,because in this kind of case the similarity value increases the most,under this supposition, when the input signal window is moved at leastthe feature vector interval (w_(k)−1), the upper bound of the totalsimilarity value S^(k) becomes the threshold value θ.

In contrast, if S^(k)≧θ, in order to find the local peak of thesimilarity value, then w_(k)=1, and a search is carried out that doesnot skip any windows.

The above described processing is for one (that is, the k) referenceaudio signal. In the case that there is a plurality of reference audiosignals, in the conventional method, the skip width for each referenceaudio signal is found by simply repeating the processing at least thenumber of times equal to the number of reference audio signals (this isknown as the iteration method). In contrast, when the similarity valueS^(k) for the k^(th) reference audio signal is obtained, the presentmethod is characterized in finding the skip width for a reference audiosignal other then the k^(th) signal based on the similarity value S^(k).Thus, the number of similarity value computations can be decreased incomparison to the iteration method.

The following is an explanation of the method of decreasing the numberof similarity value computations. The histograms G^(k) and G^(m) areproduced respectively for the two reference audio signals R^(k) andR^(m), and the histogram is prepared from the input audio signal I.

Here, it is assumed that the total number of histograms G^(k) and G^(m)is equal, and the reference audio signal and the input audio signal arenot partitioned in the direction of the time axis. In addition, it isalso assumed that the input signal similarity value S^(k) between thehistogram G^(k) and the histogram H is already known.

At this time, if the histogram G^(k) and the histogram G^(m) resembleeach other closely, the input signal similarity value S^(m) between thehistogram H and the histogram G^(m) is not calculated, and the upperbound of the input signal similarity value S^(m) is obtained. That is,if the reference signal similarity value between histogram G^(k) andhistogram G^(m) is S^(km), when all of the parts that do not matchbetween the histogram G^(k) and the histogram G^(m) contribute to theincrease in the input signal similarity value S^(m), the input signalsimilarity value S^(m) has reached its upper bound. This is shown by thefollowing equation:S ^(m) ≦S ^(k)+(1−S ^(km))  (8)Here, because originally S^(m)≦1, Eq. 8 is only valid when S^(k)<S^(km).

In contrast, if the histogram G^(k) and the histogram H resemble eachother closely, when all of the parts that do not match between thehistogram G^(k) and the histogram H contribute to the increase in theinput signal similarity value S^(m), the input signal similarity valueS^(m) has reached its upper bound. This is shown by the followingequation:S ^(m) ≦S ^(km)+(1−S ^(k))  (9)Here, because originally S^(m)≦1, Eq. 9 is only valid when S^(k)<S^(km).

When Eq. 8 and Eq. 9 are combined, the following equation is obtained:S ^(m)≦1−|S ^(k) −S ^(km)  (10)

That is, the larger the difference between the input signal similarityvalue S^(k) and the reference signal similarity value S^(km), thesmaller the upper bound of the input signal similarity value S^(m). Inthis situation, the skip width w_(m) becomes large. $\begin{matrix}{w_{m} = \left\{ \begin{matrix}{{{floor}\quad\left( {D\left( {\theta - S^{m}} \right)} \right)} + 1} & \left( {S^{m} < \theta} \right) \\1 & ({otherwise})\end{matrix} \right.} & (11)\end{matrix}$That is, the smaller the input signal similarity value S^(m), the largerthe skip width w=.

As described above, according to the present invention, if theinter-reference signal value S^(km) of the two reference audio signalsR^(k) and R^(m) is known, when the input signal similarity value S^(k)between the reference audio signal R^(k) and the input audio signal I isobtained, it is possible to find the skip width for the reference audiosignal R^(m), not just the skip width for the reference audio signalR^(k) based on this input signal similarity value k. Thus, each time theinput signal similarity value S^(k) between the k^(th) reference audiosignal R^(k) and the input audio signal I is obtained (in the similarityvalue calculation means 316), the skip width calculation means 318extracts (from the inter-reference signal similarity value calculationmeans 314) the inter-reference signal similarity value between thereference audio signal R^(k) and the reference audio signal other thanthis reference audio signal R^(k), and using this inter-reference signalsimilarity value and Eq. 10 and Eq. 11, the skip width (of the inputsignal window) for all reference audio signals can be found.Additionally, when the skip width found in this manner is larger thanthe skip width found up to that time, the skip width calculation means318 updates the skip width.

The similarity value calculation means 316 supplies the updated skipwidth w_(i) (i=1, . . . , N) to the input feature quantity calculationmeans 312. The input feature quantity calculation means 312 transfersonly the smallest skip width among the skip widths w_(i) to the inputsignal window (step S 309). Subsequently, the above processing(producing a histogram, calculating the similarity value, calculatingthe skip width, etc.) is repeated in the same manner (step S 310). Inaddition, when the input signal window passes the end of the input audiosignal (of the feature vector of the time series) due to the moving ofthe input signal window, the search processing ends because the entireinput audio signal has been searched.

Next, an example of the experimental operation of the high-speed signaldetection device according to the present embodiment will be explained.In order to examine the effects of the present embodiment, a searchexperiment was carried out using the audio signal of a 6-hour televisionbroadcast as the input signal, and the audio signal of a 15-secondcommercial was used as the reference signal. In the present device, onlythe number of checks will be compared since with respect to precisionthe case of checking the reference signals separately is identical. Inaddition, because a complete search is always carried out for the casein which the similarity value exceeds the threshold value, here, thesubject matter of the examination is what percentage the number ofchecks is when the similarity value is below the threshold value incomparison to the number of checks when checking reference signalsseparately (in this application, this is referred to as the “checknumber ratio”). For the parameters of the search, the samplingfrequency=11.025 kHz, the number of band filters=7, the length of thewindow for the frequency analysis=60 ms, the window shift=10 ms, thenumber of bins in each feature dimension=3, the number of windowpartitions per hour=1, and the threshold value θ=0.8. An example of thesearch results is shown in FIG. 17. The vertical axis is the similarityvalue, and the horizontal axis is the time. In addition, the mark ‘o’denotes a detected location, and the broken line is the threshold valueθ (0.8).

FIG. 18 shows the results of the experiment. Experiment example (a) inFIG. 18 is the case when the different products of three commercials arerandomly selected, experiment example (b) is the result of the case whenthe same product with very similar sound in three commercials is used asthe reference signal. The similarity values between the referencesignals is experiment example (a) were 0.11, 0.22, and 0.23, and inexperiment example (b) were 0.72, 0.75, and 0.88. In the case ofexperiment example (a), the result of the decrease in the number ofchecks is small (the check number ratio is 99.9%), and in the case ofexperiment example (b), the number of checks when the similarity valuewas below the threshold value was less than ⅔ (the check number ratiowas 62.7%).

In this manner, the present apparatus is particularly effective when thesimilarity value between reference signals is high.

Addendum

Above, the embodiments of this invention were described in detailreferring to the figures, but the concrete structure is not limited tothese embodiments, and included in this invention are such alterationsthat do not exceed the design scope of the gist of this invention.

For example, in the above-described first through third embodiments, anexplanation of a high-speed signal search device characterized by asimilarity value calculation using a histogram and a skip widthcalculation based on the similarity value, but the high-speed signalsearch method according to the present invention is not necessarilylimited to these two features, and a special effect is achieved incomparison to the conventional technology even when just one among thesefeatures is used.

In addition, in the above-described first through third embodiments, anaudio signal is used as the signal that is the object of processing, butas described in the experiment examples if FIG. 13 and FIG. 14, signalsrepresenting color information (R, G, B, etc.) of an image can also beused.

In addition, as one example of a feature quantity, in the firstembodiment an audio signal zero crossing was used, and in the second andthird embodiments spectral features were used, but the featurequantities used in the present invention are not limited thereto, andother feature quantities can be used.

In addition, in each of the above-described embodiments, the case ofcarrying out signal detection based on the similarity value of ahistogram was described, but instead of a similarity value, the signaldetection can be carried out using distance (Euclidean distance, L1distance, etc.). In this case, it goes without saying that the sizerelationships of the values in the case based on similarity values isreversed, and the same results can be obtained.

In addition, in each of the above-described embodiments, the high-speedsignal search apparatus was described as an independent unit, but it ispossible to construct an automatic control apparatus for a video usingthis apparatus. That is, combining the present high-speed signal searchdevice with a video device, at the same time a control device can beprovided that detects the generation of a specified audio signal (themesong, etc.) or a specified image pattern, etc., by the presenthigh-speed signal search device, and controls the recording function,etc., of the video device according to this search result. Thereby, itis possible to realize an automatic video control device that canautomatically activate the video recording function or stop therecording function according to the start or end of a specified program,or record indexed information.

Furthermore, in this manner, it is possible to automatically record bydetecting the time that a specified commercial is broadcast from theaudio signal of the broadcast, and start and stop video recording bydetecting a specified theme song. In addition, it is possible toautomatically monitor the time that the sound of applause occurs and thetime that laughter occurs from the broadcast, and search for a specifiedscene. Furthermore, this processing can be applied not only to audiosignals, but to general signals such as an image pattern.

1-9. (canceled)
 10. A high-speed signal search method characterized inproviding: a first step that generates a feature quantity sequence for apre-recorded reference signal; a second step that sets the referencesignal window for the feature quantity sequence generated in the firststep; a third step that partitions the reference signal window set inthe second step into a plurality of partitioned reference signalwindows; a fourth step that generates a feature quantity sequence forthe input signal that has been input; a fifth step that sets the inputsignal window for the feature quantity sequence generated in the fourthstep; a sixth step that partitions this input signal window into aplurality of partitioned input signal windows corresponding to theplurality of partitioned reference signal windows; a seventh step thatcalculates the input signal similarity value showing the degree ofsimilarity between the feature quantity sequence of each referencesignal partition window and the feature quantity sequence in thepartitioned input signal windows corresponding to the relevantpartitioned reference signal window; an eighth step that calculates askip width indicating the amount that the input signal window can bemoved based on the input signal similarity value calculated in theseventh step; and a ninth step which determines the position of theinput signal window based on the skip width calculated in the eighthstep and sets the input signal window at that position; and further,calculates an input signal similarity value for each position of theinput signal window by repeating the sixth step to the ninth step; anddetermines whether or not the reference signal exists at the positionthat the input signal window presently shows in the input signal basedon the result of comparing the input signal similarity value and thepredetermined threshold value.
 11. A high-speed signal search methodaccording to claim 10 characterized in providing: a pre-check step whichfinds in advance the similarity value between an input signal and areference signal for a plurality of locations on said input signal;pre-check similarity value statistics step which find the mean and thestandard deviation of this similarity value for the plurality ofsimilarity values obtained in said pre-check step; a threshold valuedetermination step which determines said threshold value based on themean and standard deviation obtained in said pre-check similarity valuestatistics step.
 12. A high-speed signal search method according toclaim 10 characterized in said input signal similarity value being theintersection similarity value.
 13. A high-speed signal search methodaccording to claim 10 characterized in said input signal window havingthe same time length as said reference signal in the direction of thetime axis.
 14. A high-speed signal search method according to claim 10characterized in said skip width being calculated by$w = \left\{ \begin{matrix}{{{floor}\quad\left( {D\left( {\theta - S} \right)} \right)} + 1} & \left( {S < \theta} \right) \\1 & ({otherwise})\end{matrix} \right.$ where: 0 is said threshold value; S is said inputsignal similarity value; D is the total frequency of said featurequantity sequence; and Floor { } denotes the cutoff for rounding off thenumber.
 15. A high-speed signal search method according to claim 10characterized in said reference signal and said input signal being anaudio signal.
 16. A high-speed signal search method characterized inproviding: a first step that generates a feature quantity sequence for apre-recorded reference signal; a second step that sets a referencesignal window for the feature quantity sequence generated in the firststep; a third step that partitions the reference signal window set inthe second step into a plurality of partitioned reference signalwindows; a fourth step that generates a feature quantity sequence forthe input signal that has been input; a fifth step that sets the inputsignal window for the feature quantity sequence generated in the fourthstep; a sixth step that partitions this input signal window into aplurality of partitioned input signal windows corresponding tot hisplurality of partitioned reference signal windows; a seventh step thatproduces a histogram of the feature quantity sequence of eachpartitioned reference signal window; an eighth step that produces ahistogram of a feature quantity sequence of each partitioned inputsignal window; a ninth step that calculates the input signal similarityvalue showing the degree of similarity between the histogram of eachpartitioned reference signal window and the histogram of the partitionedinput signal window corresponding to the relevant partitioned referencesignal window; a tenth step that calculates the skip width showing theamount that the input signal window can move based on the input signalsimilarity value calculated in the ninth step; and an eleventh step thatdetermines the position of the input signal window and sets the inputsignal window at that position; and further, calculates an input signalsimilarity value for each position of the input signal window byrepeating the sixth step to the eleventh step; and determines whether ornot the reference signal exists at the position that the input signalwindow presently shows in the input signal based on the result ofcomparing the input signal similarity value and the predeterminedthreshold value.
 17. A high-speed signal search method according toclaim 16 characterized in: said feature quantity sequence being asequence of zero-crossing feature quantities of the reference signal andthe input signal, and its integral value; and said histogram beingproduced by partitioning the range of value that said zero-crossingnumber and its integral value can take into a plurality of bins, andcalculates the feature quantity sequence corresponding to each of saidbins based on said zero-crossing number and its integral value.
 18. Ahigh-speed signal search method according to claim 16 characterized in:said feature quantity sequence is a sequence of feature vectors having aplurality of frequency band components as elements; and said histogrambeing produced by partitioning the range of values that each of theelements of said feature vector can take into a plurality of bins, andcalculating the feature quantity sequence corresponding to each of saidbins based on the value of said elements. 19-39. (canceled)